Instructions to use aditira/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aditira/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="aditira/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("aditira/audio_classification") model = AutoModelForAudioClassification.from_pretrained("aditira/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aditira/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/aditira/audio_classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://aditira/audio_classification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aditira/audio_classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
378 MB
- Xet hash:
- e5e75a55f20ffba08b1dcbf7a146c922efb87b40d24e119671a178ef0c728e29
- Size of remote file:
- 378 MB
- SHA256:
- 2703374955a6df59528acabbabc66d2a0b03b4e6d05bfb6ebea210c86dc558d3
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